Accelerate your AI Financial Modelling with IPU
In the finance sector, the potential for innovation with advanced machine intelligence is significant. But often, new and complex models are not being fully leveraged due to latency issues and compute restraints. Enter the IPU – a completely new processing architecture designed for machine intelligence, capable of running advanced financial models up to 26x faster. Helen Byrne from Graphcore explains how the IPU’s unique architecture can power such incredible breakthroughs – and what this means for the future of finance and trading.
What you’ll learn:
How the IPU is able to achieve faster financial model accelerations than other hardware available on the market How to use IPUs for financial modelling training and inference Insights into advanced models, use cases and IPU benchmarks
Helen has been an AI Research Engineer at Graphcore since July 2018. She has a BSc in Mathematics (from the University of Bristol) and a Master’s degree in Artificial Intelligence. Before finding her passion for research at Graphcore, she was a Maths teacher and worked at an Investment Banking FinTech. She is a member of the Graphcore Research team who are working at the cutting edge of fundamental and applied AI research. Helen is currently working on distributed learning in large-scale machines.